This notebook gives an overview of demogrpahic, socioeconomic and health-related datasets that might help with the research on COVID-19 in the U.S.
library(tidyverse)The following demonstrated datasets are from U.S. Department of Health and Human Services.
medicare (https://data.medicare.gov), which documents the general information on health facilities across the United States, is part of the dataset on the Medicare.gov Hospital Compare Website provided by the Centers for Medicare & Medicaid Services to compare the quality of care at over 4000 medicare-certified hospitals across the country. More data packages on medicare and hospitals in the U.S. are available in the same folder. This is updated on 4/22/2020.
medicare <- read.csv("data/Department of Health and Human Services/Hospital_Revised_Flatfiles/Hospital General Information.csv")
medicareNAglimpse(medicare)Rows: 5,326
Columns: 28
$ Facility.ID [3m[38;5;246m<fct>[39m[23m 01…
$ Facility.Name [3m[38;5;246m<fct>[39m[23m SO…
$ Address [3m[38;5;246m<fct>[39m[23m "1…
$ City [3m[38;5;246m<fct>[39m[23m DO…
$ State [3m[38;5;246m<fct>[39m[23m AL…
$ ZIP.Code [3m[38;5;246m<int>[39m[23m 36…
$ County.Name [3m[38;5;246m<fct>[39m[23m HO…
$ Phone.Number [3m[38;5;246m<fct>[39m[23m (3…
$ Hospital.Type [3m[38;5;246m<fct>[39m[23m Ac…
$ Hospital.Ownership [3m[38;5;246m<fct>[39m[23m Go…
$ Emergency.Services [3m[38;5;246m<fct>[39m[23m Ye…
$ Meets.criteria.for.promoting.interoperability.of.EHRs [3m[38;5;246m<fct>[39m[23m Y,…
$ Hospital.overall.rating [3m[38;5;246m<fct>[39m[23m 2,…
$ Hospital.overall.rating.footnote [3m[38;5;246m<int>[39m[23m NA…
$ Mortality.national.comparison [3m[38;5;246m<fct>[39m[23m Be…
$ Mortality.national.comparison.footnote [3m[38;5;246m<int>[39m[23m NA…
$ Safety.of.care.national.comparison [3m[38;5;246m<fct>[39m[23m Sa…
$ Safety.of.care.national.comparison.footnote [3m[38;5;246m<int>[39m[23m NA…
$ Readmission.national.comparison [3m[38;5;246m<fct>[39m[23m Be…
$ Readmission.national.comparison.footnote [3m[38;5;246m<int>[39m[23m NA…
$ Patient.experience.national.comparison [3m[38;5;246m<fct>[39m[23m Be…
$ Patient.experience.national.comparison.footnote [3m[38;5;246m<int>[39m[23m NA…
$ Effectiveness.of.care.national.comparison [3m[38;5;246m<fct>[39m[23m Sa…
$ Effectiveness.of.care.national.comparison.footnote [3m[38;5;246m<int>[39m[23m NA…
$ Timeliness.of.care.national.comparison [3m[38;5;246m<fct>[39m[23m Sa…
$ Timeliness.of.care.national.comparison.footnote [3m[38;5;246m<int>[39m[23m NA…
$ Efficient.use.of.medical.imaging.national.comparison [3m[38;5;246m<fct>[39m[23m Sa…
$ Efficient.use.of.medical.imaging.national.comparison.footnote [3m[38;5;246m<int>[39m[23m NA…medicareDisparityPop, if mapped as demonstrated in the link (https://data.cms.gov/mapping-medicare-disparities), can be used to 1) visualize health outcome measures at a national, state, or county level; 2) explore health outcome measures by age, race and ethnicity, sex; 3) compare differences between two geographic locations (e.g., benchmark against the national average); and 4) compare differences between two racial and ethnic groups within the same geographic area.
This is a population view, last updated on 6/17/2019. Hospital-wise mapped comparisons are available here: https://data.cms.gov/mapping-medicare-disparities/hospital-view. Information that is used to make this map can be extracted from the “Hospital_Revised_Flatfiles” folder.
medicareDisparityPop <- read.csv("data/Department of Health and Human Services/mmd_data.csv")
medicareDisparityPopglimpse(medicareDisparityPop)Rows: 2,656
Columns: 17
$ year [3m[38;5;246m<int>[39m[23m 2017, 2017, 2017, 2017, 2017, 2017, 2017, 20…
$ geography [3m[38;5;246m<fct>[39m[23m County, County, County, County, County, Coun…
$ measure [3m[38;5;246m<fct>[39m[23m Average principal cost, Average principal co…
$ adjustment [3m[38;5;246m<fct>[39m[23m Unsmoothed actual, Unsmoothed actual, Unsmoo…
$ analysis [3m[38;5;246m<fct>[39m[23m Base measure, Base measure, Base measure, Ba…
$ domain [3m[38;5;246m<fct>[39m[23m Primary chronic conditions, Primary chronic …
$ condition [3m[38;5;246m<fct>[39m[23m Acute myocardial infarction, Acute myocardia…
$ primary_sex [3m[38;5;246m<fct>[39m[23m All, All, All, All, All, All, All, All, All,…
$ primary_age [3m[38;5;246m<fct>[39m[23m All, All, All, All, All, All, All, All, All,…
$ primary_dual [3m[38;5;246m<fct>[39m[23m Dual & non-dual, Dual & non-dual, Dual & non…
$ fips [3m[38;5;246m<int>[39m[23m 1001, 1003, 1005, 1007, 1009, 1013, 1015, 10…
$ county [3m[38;5;246m<fct>[39m[23m Autauga County, Baldwin County, Barbour Coun…
$ state [3m[38;5;246m<fct>[39m[23m ALABAMA, ALABAMA, ALABAMA, ALABAMA, ALABAMA,…
$ urban [3m[38;5;246m<fct>[39m[23m Urban, Rural, Rural, Urban, Urban, Rural, Ur…
$ primary_race [3m[38;5;246m<fct>[39m[23m All, All, All, All, All, All, All, All, All,…
$ primary_denominator [3m[38;5;246m<fct>[39m[23m undefined, undefined, undefined, undefined, …
$ analysis_value [3m[38;5;246m<int>[39m[23m 14426, 11940, 6814, 10223, 7214, 13817, 1021…A helpful csv on American geographies and demographies (https://data.cms.gov/mapping-medicare-disparities) that is also used to generate the map in the link.
geography <- read.csv("data/Department of Health and Human Services/ACS_Profile_Data_20180801.csv")
geographyglimpse(geography)Rows: 468,016
Columns: 9
$ year [3m[38;5;246m<int>[39m[23m 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 20…
$ fips [3m[38;5;246m<int>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ state [3m[38;5;246m<fct>[39m[23m USA, USA, USA, USA, USA, USA, USA, USA, USA, USA, …
$ county [3m[38;5;246m<fct>[39m[23m , , , , , , , , , , , , , , , , , , , , , , , ,
$ numerator [3m[38;5;246m<int>[39m[23m 263919712, 461580, 13403056, 8871774, 25081122, 13…
$ denominator [3m[38;5;246m<dbl>[39m[23m 289000832, 7258907, 31631926, 31631926, 289000832,…
$ value [3m[38;5;246m<dbl>[39m[23m 9.132144e-01, 6.358808e-02, 4.237193e-01, 2.804690…
$ measure_id [3m[38;5;246m<int>[39m[23m 14, 31, 18, 25, 13, 19, 28, 22, 8, 33, 17, 32, 16,…
$ measure_label [3m[38;5;246m<fct>[39m[23m Percentage of Population Who Speak English 'Very W…Updated on 8/22/2018, careState provides medical care program names, program features, population enrolled, benefits covered, quality assurance and improvement, performance incentives, provider value-based purchasing, participating plans, regions served and program notes (https://data.medicaid.gov/Enrollment/Managed-Care-Programs-by-State/p9c7-tuup).
careState <- read.csv("data/Department of Health and Human Services/Managed_Care_Programs_by_State.csv")
careStateglimpse(careState)Rows: 195
Columns: 69
$ State [3m[38;5;246m<fct>[39m[23m …
$ Program.Name [3m[38;5;246m<fct>[39m[23m …
$ Program.type [3m[38;5;246m<fct>[39m[23m …
$ Statewide.or.region.specific. [3m[38;5;246m<fct>[39m[23m …
$ Federal.operating.authority [3m[38;5;246m<fct>[39m[23m …
$ Program.start.date [3m[38;5;246m<fct>[39m[23m …
$ Waiver.expiration.date..if.applicable. [3m[38;5;246m<fct>[39m[23m …
$ If.the.program.ended.in.2014..indicate.the.end.date [3m[38;5;246m<fct>[39m[23m …
$ Populations.enrolled [3m[38;5;246m<fct>[39m[23m …
$ Low.income.Adults.not.eligible.under.ACA.Section.VIII [3m[38;5;246m<fct>[39m[23m …
$ Aged..Blind.or.Disabled.Children.or.Adults [3m[38;5;246m<fct>[39m[23m …
$ Non.Disabled.Children..excluding.children.in.foster.care.or.receiving.adoption.assistance. [3m[38;5;246m<fct>[39m[23m …
$ Individuals.receiving.Limited.Benefits [3m[38;5;246m<fct>[39m[23m …
$ Low.income.adults.eligible.under.ACA.Section.VIII [3m[38;5;246m<fct>[39m[23m …
$ Full.Duals [3m[38;5;246m<fct>[39m[23m …
$ Partial.Duals [3m[38;5;246m<fct>[39m[23m …
$ Children.with.Special.Health.Care.Needs [3m[38;5;246m<fct>[39m[23m …
$ Native.American.Alaskan.Natives [3m[38;5;246m<fct>[39m[23m …
$ Foster.Care.and.Adoption.Assistance.Children [3m[38;5;246m<fct>[39m[23m …
$ Enrollment.choice.period [3m[38;5;246m<fct>[39m[23m …
$ Enrollment.broker.name..if.applicable. [3m[38;5;246m<fct>[39m[23m …
$ Notes.on.enrollment.choice.period [3m[38;5;246m<fct>[39m[23m …
$ Benefits.covered [3m[38;5;246m<fct>[39m[23m …
$ Inpatient.hospital.physical.health [3m[38;5;246m<fct>[39m[23m …
$ Inpatient.hospital.behavioral.health..MH.and.or.SUD. [3m[38;5;246m<fct>[39m[23m …
$ Outpatient.hospital.physical.health [3m[38;5;246m<fct>[39m[23m …
$ Outpatient.hospital.behavioral.health..MH.and.or.SUD. [3m[38;5;246m<fct>[39m[23m …
$ Partial.hospitalization [3m[38;5;246m<fct>[39m[23m …
$ Physician [3m[38;5;246m<fct>[39m[23m …
$ Nurse.practitioner [3m[38;5;246m<fct>[39m[23m …
$ Rural.health.clinics.and.FQHCs [3m[38;5;246m<fct>[39m[23m …
$ Clinic.services [3m[38;5;246m<fct>[39m[23m …
$ Lab.and.x.ray [3m[38;5;246m<fct>[39m[23m …
$ Prescription.drugs.and.prosthetic.devices [3m[38;5;246m<fct>[39m[23m …
$ EPSDT [3m[38;5;246m<fct>[39m[23m …
$ Case.management [3m[38;5;246m<fct>[39m[23m …
$ Health.home..SSA.1945. [3m[38;5;246m<fct>[39m[23m …
$ Family.planning [3m[38;5;246m<fct>[39m[23m …
$ Dental.services..medical.surgical. [3m[38;5;246m<fct>[39m[23m …
$ Dental..preventative.or.corrective. [3m[38;5;246m<fct>[39m[23m …
$ Home.health.agency.services [3m[38;5;246m<fct>[39m[23m …
$ Personal.care..state.plan.option. [3m[38;5;246m<fct>[39m[23m …
$ HCBS.waiver.services [3m[38;5;246m<fct>[39m[23m …
$ Private.duty.nursing [3m[38;5;246m<fct>[39m[23m …
$ ICF.IDD [3m[38;5;246m<fct>[39m[23m …
$ Nursing.facility.services [3m[38;5;246m<fct>[39m[23m …
$ Hospice.care [3m[38;5;246m<fct>[39m[23m …
$ Non.Emergency.Medical.Transportation [3m[38;5;246m<fct>[39m[23m …
$ Other..e.g...nurse.midwife.services..freestanding.birth.centers..podiatry..etc.. [3m[38;5;246m<fct>[39m[23m …
$ Quality.assurance.and.improvement [3m[38;5;246m<fct>[39m[23m …
$ HEDIS.data.required. [3m[38;5;246m<fct>[39m[23m …
$ CAHPS.data.required. [3m[38;5;246m<fct>[39m[23m …
$ Accreditation.required. [3m[38;5;246m<fct>[39m[23m …
$ Accrediting.organization [3m[38;5;246m<fct>[39m[23m …
$ EQRO.contractor.name..if.applicable. [3m[38;5;246m<fct>[39m[23m …
$ Performance.incentives. [3m[38;5;246m<fct>[39m[23m …
$ Payment.bonuses.differentials.to.reward.plans [3m[38;5;246m<fct>[39m[23m …
$ Preferential.auto.enrollment.to.reward.plans [3m[38;5;246m<fct>[39m[23m …
$ Public.reports.comparing.MCO.performance.on.key.metrics [3m[38;5;246m<fct>[39m[23m …
$ Withholds.tied.to.performance.metrics [3m[38;5;246m<fct>[39m[23m …
$ MCOs.PHPs.required.encouraged.to.pay.providers.for.value.quality.outcomes.using.shared.risk.or.shared.savings.methods [3m[38;5;246m<fct>[39m[23m …
$ Provider.Value.Based.Purchasing [3m[38;5;246m<fct>[39m[23m …
$ State.pays.provider.based.entities..such.as.ACOs.or.PCMHs..directly.for.value.quality.outcomes.using.shared.risk.or.shared.savings.methods [3m[38;5;246m<fct>[39m[23m …
$ Participating.plans.and.regions.served [3m[38;5;246m<fct>[39m[23m …
$ Plans.in.Program [3m[38;5;246m<fct>[39m[23m …
$ Notes [3m[38;5;246m<fct>[39m[23m …
$ Program.notes [3m[38;5;246m<fct>[39m[23m …
$ Year [3m[38;5;246m<int>[39m[23m …
$ Location [3m[38;5;246m<fct>[39m[23m …The Market Saturation and Utilization Data Tool was developed to allow the Centers for Medicare & Medicaid Services (CMS) to monitor market saturation as a means to help prevent potential fraud, waste, and abuse (FWA). Market saturation refers to the density of providers of a particular service within a defined geographic area relative to the number of beneficiaries receiving that service in the area. The data can be used to reveal the degree to which use of a service is related to the number of providers servicing a geographic region (data and texts from https://data.cms.gov/market-saturation/states-and-counties/methodology.
beneMarket <- read.csv("data/Department of Health and Human Services/Market_Saturation_And_Utilization_Dataset_2020-01-07.csv")
beneMarketglimpse(beneMarket)Rows: 747,944
Columns: 32
$ Reference.Period [3m[38;5;246m<fct>[39m[23m …
$ Type.of.Service [3m[38;5;246m<fct>[39m[23m …
$ Aggregation.Level [3m[38;5;246m<fct>[39m[23m …
$ State [3m[38;5;246m<fct>[39m[23m …
$ County [3m[38;5;246m<fct>[39m[23m …
$ State.FIPS [3m[38;5;246m<int>[39m[23m …
$ County.FIPS [3m[38;5;246m<int>[39m[23m …
$ Number.of.Fee.for.Service.Beneficiaries [3m[38;5;246m<int>[39m[23m …
$ Number.of.Providers [3m[38;5;246m<int>[39m[23m …
$ Average.Number.of.Users.per.Provider [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.Users.out.of.FFS.Beneficiaries [3m[38;5;246m<dbl>[39m[23m …
$ Number.of.Users [3m[38;5;246m<int>[39m[23m …
$ Average.Number.of.Providers.per.County [3m[38;5;246m<dbl>[39m[23m …
$ Number.of.Dual.Eligible.Users [3m[38;5;246m<int>[39m[23m …
$ Percentage.of.Dual.Eligible.Users.out.of.Total.Users [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.Dual.Eligible.Users.out.of.FFS.Beneficiaries [3m[38;5;246m<dbl>[39m[23m …
$ Total.Payment [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.FFS.Beneficiaries.By.Number.of.Providers.Serving.County...0.to.2.Providers [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.FFS.Beneficiaries.By.Number.of.Providers.Serving.County...3.to.4.Providers [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.FFS.Beneficiaries.By.Number.of.Providers.Serving.County...5.to.9.Providers [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.FFS.Beneficiaries.By.Number.of.Providers.Serving.County...10.to.19.Providers [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.FFS.Beneficiaries.By.Number.of.Providers.Serving.County...20.or.More.Providers [3m[38;5;246m<dbl>[39m[23m …
$ Number.of.Fee.for.Service.Beneficiaries...Change [3m[38;5;246m<dbl>[39m[23m …
$ Number.of.Providers...Change [3m[38;5;246m<dbl>[39m[23m …
$ Average.Number.of.Users.per.Provider...Change [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.Users.out.of.FFS.Beneficiaries...Change [3m[38;5;246m<dbl>[39m[23m …
$ Number.of.Users...Change [3m[38;5;246m<dbl>[39m[23m …
$ Average.Number.of.Providers.per.County...Change [3m[38;5;246m<dbl>[39m[23m …
$ Number.of.Dual.Eligible.Users...Change [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.Dual.Eligible.Users.out.of.Total.Users...Change [3m[38;5;246m<dbl>[39m[23m …
$ Percentage.of.Dual.Eligible.Users.out.of.Dual.Eligible.FFS.Beneficiaries...Change [3m[38;5;246m<dbl>[39m[23m …
$ Total.Payments...Change [3m[38;5;246m<dbl>[39m[23m …Updated on 3/11/2020, the following file is a list of long-term care hospitals with general information such as address, phone number, ownership data, bed counts and more (https://data.medicare.gov/Long-Term-Care-Hospital-Compare/Long-Term-Care-Hospital-General-Information/azum-44iv).
longTermHospitalGeneral <- read.csv("data/Department of Health and Human Services/Long-_Term_Care_Hospital_-_General_Information.csv")
longTermHospitalGeneralglimpse(longTermHospitalGeneral)Rows: 377
Columns: 14
$ CMS.Certification.Number..CCN. [3m[38;5;246m<int>[39m[23m 112012, 292003, 452034, 452063, …
$ Facility.Name [3m[38;5;246m<fct>[39m[23m "COLUMBUS SPECIALTY HOSPITAL, IN…
$ Address.Line.1 [3m[38;5;246m<fct>[39m[23m "616 19TH STREEET", "640 S MARTI…
$ Address.Line.2 [3m[38;5;246m<lgl>[39m[23m NA, NA, NA, NA, NA, NA, NA, NA, …
$ City [3m[38;5;246m<fct>[39m[23m COLUMBUS, LAS VEGAS, AUSTIN, MC …
$ State [3m[38;5;246m<fct>[39m[23m GA, NV, TX, TX, FL, UT, LA, IN, …
$ Zip.Code [3m[38;5;246m<int>[39m[23m 31901, 89106, 78756, 78504, 3341…
$ County.Name [3m[38;5;246m<fct>[39m[23m Muscogee, Clark, Travis, Hidalgo…
$ PhoneNumber [3m[38;5;246m<fct>[39m[23m (706) 494-4075, (702) 382-3155, …
$ CMS.Region [3m[38;5;246m<int>[39m[23m 4, 9, 6, 6, 4, 8, 6, 5, 6, 6, 5,…
$ Ownership.Type [3m[38;5;246m<fct>[39m[23m For profit, For profit, For prof…
$ Certification.Date [3m[38;5;246m<fct>[39m[23m 01/01/2004, 06/01/1994, 03/01/19…
$ Total.Number.of.Beds [3m[38;5;246m<int>[39m[23m 30, 199, 157, 84, 70, 41, 24, 45…
$ Location [3m[38;5;246m<fct>[39m[23m "", "", "4207 BURNET RD AUSTIN, …The following packages are from Harvard Global Health Institute in its research on state-level hospital bed capacity & COVID-19 estimates on 3/17/2020. The research makes localized predictions on the number of beds and ICU units, and additional capacity needed to accommodate COVID-19 patients over the coming months. (https://globalepidemics.org/our-data/hospital-capacity/)
This is a state-wise census and predictions on hospital resources states currently have and potentially need in different COVID-19 infection scenario.
hospitalBed <- read.csv("data/Hospital Capacity by State ( 20% _ 40% _ 60% ) - 20%.csv")
hospitalBedglimpse(hospitalBed)Rows: 54
Columns: 38
$ State [3m[38;5;246m<fct>[39m[23m …
$ Total.Hospital.Beds [3m[38;5;246m<fct>[39m[23m …
$ Total.ICU.Beds [3m[38;5;246m<fct>[39m[23m …
$ Hospital.Bed.Occupancy.Rate [3m[38;5;246m<dbl>[39m[23m …
$ ICU.Bed.Occupancy.Rate [3m[38;5;246m<dbl>[39m[23m …
$ Available.Hospital.Beds [3m[38;5;246m<fct>[39m[23m …
$ Potentially.Available.Hospital.Beds. [3m[38;5;246m<fct>[39m[23m …
$ Available.ICU.Beds [3m[38;5;246m<fct>[39m[23m …
$ Potentially.Available.ICU.Beds. [3m[38;5;246m<fct>[39m[23m …
$ Adult.Population [3m[38;5;246m<fct>[39m[23m …
$ Population.65. [3m[38;5;246m<fct>[39m[23m …
$ Projected.Infected.Individuals [3m[38;5;246m<fct>[39m[23m …
$ Proejcted.Hospitalized.Individuals [3m[38;5;246m<fct>[39m[23m …
$ Projected.Individuals.Needing.ICU.Care [3m[38;5;246m<fct>[39m[23m …
$ Hospital.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Hospital.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Hospital.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ ICU.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.ICU.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.ICU.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.ICU.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ ICU.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.ICU.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.ICU.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.ICU.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ ICU.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.ICU.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.ICU.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.ICU.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …hospitalBedCounty is data on county-level, from the same source.
hospitalBedCounty <- read.csv("data/HRR Scorecard_ 20% _ 40% _ 60% - 20% Population.csv")
hospitalBedCountyglimpse(hospitalBedCounty)Rows: 306
Columns: 36
$ HRR [3m[38;5;246m<fct>[39m[23m …
$ Total.Hospital.Beds [3m[38;5;246m<fct>[39m[23m …
$ Total.ICU.Beds [3m[38;5;246m<fct>[39m[23m …
$ Available.Hospital.Beds [3m[38;5;246m<fct>[39m[23m …
$ Potentially.Available.Hospital.Beds. [3m[38;5;246m<fct>[39m[23m …
$ Available.ICU.Beds [3m[38;5;246m<int>[39m[23m …
$ Potentially.Available.ICU.Beds. [3m[38;5;246m<fct>[39m[23m …
$ Adult.Population [3m[38;5;246m<fct>[39m[23m …
$ Population.65. [3m[38;5;246m<fct>[39m[23m …
$ Projected.Infected.Individuals [3m[38;5;246m<fct>[39m[23m …
$ Projected.Hospitalized.Individuals [3m[38;5;246m<fct>[39m[23m …
$ Projected.Individuals.Needing.ICU.Care [3m[38;5;246m<fct>[39m[23m …
$ Hospital.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Hospital.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Hospital.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ ICU.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.ICU.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.ICU.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.ICU.Beds.Needed..Six.Months [3m[38;5;246m<fct>[39m[23m …
$ ICU.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.ICU.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.ICU.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.ICU.Beds.Needed..Twelve.Months [3m[38;5;246m<fct>[39m[23m …
$ ICU.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Available.ICU.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Potentially.Available.ICU.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …
$ Percentage.of.Total.ICU.Beds.Needed..Eighteen.Months [3m[38;5;246m<fct>[39m[23m …note A national statistical account of hospital bed counts is available here: https://usafacts.org/data/topics/people-society/health/hospitals-and-visits-2/hospital-beds/.
Hospital-wise hospital data from U.S. Department of Homeland Security (https://hifld-geoplatform.opendata.arcgis.com/datasets/hospitals):
hospital <- read.csv("data/Hospitals.csv")
hospitalglimpse(hospital)Rows: 7,581
Columns: 32
$ FID [3m[38;5;246m<int>[39m[23m 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
$ ID [3m[38;5;246m<int>[39m[23m 5793230, 53391362, 11190023, 17090028, 23691706, 251…
$ NAME [3m[38;5;246m<fct>[39m[23m CENTRAL VALLEY GENERAL HOSPITAL, LOS ROBLES HOSPITAL…
$ ADDRESS [3m[38;5;246m<fct>[39m[23m "1025 NORTH DOUTY STREET", "150 VIA MERIDA", "4060 W…
$ CITY [3m[38;5;246m<fct>[39m[23m HANFORD, WESTLAKE VILAGE, LOS ANGELES, HOLLYWOOD, BA…
$ STATE [3m[38;5;246m<fct>[39m[23m CA, CA, CA, CA, CA, CA, CA, CA, CA, CA, CA, CA, CA, …
$ ZIP [3m[38;5;246m<int>[39m[23m 93230, 91362, 90023, 90028, 91706, 90712, 91016, 917…
$ ZIP4 [3m[38;5;246m<fct>[39m[23m NOT AVAILABLE, NOT AVAILABLE, NOT AVAILABLE, NOT AVA…
$ TELEPHONE [3m[38;5;246m<fct>[39m[23m NOT AVAILABLE, NOT AVAILABLE, NOT AVAILABLE, (323) 4…
$ TYPE [3m[38;5;246m<fct>[39m[23m GENERAL ACUTE CARE, GENERAL ACUTE CARE, GENERAL ACUT…
$ STATUS [3m[38;5;246m<fct>[39m[23m CLOSED, OPEN, OPEN, OPEN, OPEN, OPEN, OPEN, OPEN, OP…
$ POPULATION [3m[38;5;246m<int>[39m[23m 49, 62, 127, 100, 95, 172, 49, 101, 16, 78, 87, 269,…
$ COUNTY [3m[38;5;246m<fct>[39m[23m KINGS, VENTURA, LOS ANGELES, LOS ANGELES, LOS ANGELE…
$ COUNTYFIPS [3m[38;5;246m<fct>[39m[23m 06031, 06111, 06037, 06037, 06037, 06037, 06037, 060…
$ COUNTRY [3m[38;5;246m<fct>[39m[23m USA, USA, USA, USA, USA, USA, USA, USA, USA, USA, US…
$ LATITUDE [3m[38;5;246m<dbl>[39m[23m 36.33616, 34.15494, 34.02365, 34.09639, 34.06304, 33…
$ LONGITUDE [3m[38;5;246m<dbl>[39m[23m -119.64567, -118.81574, -118.18416, -118.32523, -117…
$ NAICS_CODE [3m[38;5;246m<int>[39m[23m 622110, 622110, 622110, 622110, 622110, 622110, 6221…
$ NAICS_DESC [3m[38;5;246m<fct>[39m[23m "GENERAL MEDICAL AND SURGICAL HOSPITALS", "GENERAL M…
$ SOURCE [3m[38;5;246m<fct>[39m[23m http://www.oshpd.ca.gov/HID/Facility-Listing.html, h…
$ SOURCEDATE [3m[38;5;246m<fct>[39m[23m 2016/02/04 00:00:00, 2018/08/08 00:00:00, 2018/08/08…
$ VAL_METHOD [3m[38;5;246m<fct>[39m[23m IMAGERY/OTHER, IMAGERY/OTHER, IMAGERY/OTHER, IMAGERY…
$ VAL_DATE [3m[38;5;246m<fct>[39m[23m 2014/02/10 00:00:00, 2014/02/10 00:00:00, 2014/02/10…
$ WEBSITE [3m[38;5;246m<fct>[39m[23m http://www.hanfordhealth.com, http://www.losroblesho…
$ STATE_ID [3m[38;5;246m<fct>[39m[23m NOT AVAILABLE, NOT AVAILABLE, NOT AVAILABLE, NOT AVA…
$ ALT_NAME [3m[38;5;246m<fct>[39m[23m NOT AVAILABLE, NOT AVAILABLE, NOT AVAILABLE, HOLLYWO…
$ ST_FIPS [3m[38;5;246m<int>[39m[23m 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6…
$ OWNER [3m[38;5;246m<fct>[39m[23m PROPRIETARY, PROPRIETARY, PROPRIETARY, PROPRIETARY, …
$ TTL_STAFF [3m[38;5;246m<int>[39m[23m -999, -999, -999, -999, -999, -999, -999, -999, -999…
$ BEDS [3m[38;5;246m<int>[39m[23m 49, 62, 127, 100, 95, 172, 49, 101, 16, 78, 87, 269,…
$ TRAUMA [3m[38;5;246m<fct>[39m[23m NOT AVAILABLE, NOT AVAILABLE, NOT AVAILABLE, NOT AVA…
$ HELIPAD [3m[38;5;246m<fct>[39m[23m N, N, N, N, N, N, N, N, N, N, Y, N, N, N, N, N, N, N…Demography package is from the project “Mapping the Burden of COVID-19 in the United States” (https://github.com/ianfmiller/covid19-burden-mapping).
demography <- read.csv("data/covid19-burden-mapping-master/data/us.demog.data.csv")
demography